Results 81 to 90 of about 210,501 (299)
IEEE Access, 2019 This article presents a concrete mathematical analysis on Information-Theoretic Metric Learning (ITML). The analysis provides a theoretical foundation for ITML, by supplying well-posedness, strong duality, and convergence.
Jooyeon Choi, Chohong Min, Byungjoon Lee
doaj +1 more source
CoRR, 2012 We propose a solution to the problem of estimating a Riemannian metric associated with a given differentiable manifold. The metric learning problem is based on minimizing the relative volume of a given set of points. We derive the details for a family of metrics on the multinomial simplex.
openaire +3 more sources
Rapid screening of staphylokinase protein variants using an unpurified cell‐free expression system
FEBS Open Bio, EarlyView.An unpurified cell‐free protein synthesis (CFPS) platform enables rapid functional screening of staphylokinase variants. Direct plasminogen‐activation assays performed in microplate format provide real‐time activity readouts, allowing rapid identification and ranking of variants with improved or reduced fibrinolytic activity without protein ...
Maria Tomková +3 more
wiley +1 more source
Metric Learning-Based Multi-Instance Multi-Label Classification With Label Correlation
IEEE Access, 2019 In multi-instance multi-label learning (MIML) problems, predicting the labels of unseen bags becomes difficult when the labels of their instances are not provided directly.
Haifeng Hu +3 more
doaj +1 more source
FEBS Open Bio, EarlyView.Activation of the mitochondrial protein OXR1 increases pSyn129 αSynuclein aggregation by lowering ATP levels and altering mitochondrial membrane potential, particularly in response to MSA‐derived fibrils. In contrast, ablation of the ER protein EMC4 enhances autophagic flux and lysosomal clearance, broadly reducing α‐synuclein aggregates.
Sandesh Neupane +11 more
wiley +1 more source
Heterogeneous Multitask Metric Learning Across Multiple Domains
, 2017 © 2012 IEEE. Distance metric learning plays a crucial role in diverse machine learning algorithms and applications. When the labeled information in a target domain is limited, transfer metric learning (TML) helps to learn the metric by leveraging the ...
Wen, Y +8 more
core +1 more source
Unsupervised Metric Learning with Synthetic Examples [PDF]
, 2020 Distance Metric Learning (DML) involves learning an embedding that brings similar examples closer while moving away dissimilar ones. Existing DML approaches make use of class labels to generate constraints for metric learning.
Dutta, Ujjal Kr +3 more
core +1 more source
Learning Distance Metrics for Entity Resolution
IEEE Access, 2018 Entity resolution (ER) is to find database records that refer to the same real-world entity. A key component for ER is to choose a proper distance (similarity) function for each database field to quantify the similarity of records.
Lingli Li +3 more
doaj +1 more source
FEBS Open Bio, EarlyView.Bioscience students were asked for their opinions on the value and teaching of skills. 204 responded that teamwork, time management and study skills are necessary to reach University, that scientific writing, research, laboratory and presentation skills are taught effectively during their studies, while other skills are gained inherently through study ...
Janella Borrell, Susan Crennell
wiley +1 more source
Improved Metric-Based Recommender by Historical Interactions
IEEE Access, 2019 A remarkable success in recommendations has been achieved by using methods based on metric learning, especially in digital marketing. However, the existing methods do not consider the relative preferences among items that users like.
Yubo Jiang, Yunfang Zhu, Xin Du, Tao Jin
doaj +1 more source